The use of radiation to treat medical conditions comprises a known area of prior art endeavor. For example, radiation therapy comprises an important component of many treatment plans for reducing or eliminating unwanted tumors. Unfortunately, applied radiation does not discriminate between unwanted structures and adjacent tissues, organs, or the like that are desired or even critical to continued survival of the patient. As a result, radiation is ordinarily applied in a carefully administered manner to at least attempt to restrict the radiation to a given target volume.
Such plans are often calculated using an iterative process. Beginning with some initial set of parameter settings, a radiation-treatment planning apparatus iteratively adjusts one or more of those settings and assesses the relative worth of the adjusted plan. An iterative approach such as this is often referred to as “optimizing” the plan (where “optimizing” should not be confused with the idea of identifying an objectively “optimum” plan that is superior to all other possible plans).
Optimizing such a plan can prove challenging as the overall computational requirements can be considerable. As one example in these regards, such a candidate treatment plan often comprises a plurality of control points (pertaining, for example, to collimator leaf settings at each of a plurality of source angles in an arc therapy application setting). In some application settings, the time required to work through such iterative calculations can result in vexing delays. These delays, in turn, can lead to expensive and undesirable equipment downtime, patient discomfort, and increased costs.
Furthermore, many existing radiation treatment-planning approaches require considerable interaction with an expert technician, physician, or the like. For example, good plans typically require adaptation of optimization objectives (which describe the end results being sought via administration of the radiation treatment) according to patient geometry per a skilled-person's input. To put this another way, it has not been ordinarily possible to simply specify optimization objectives for radiation-treatment plan optimization regardless of the individual patient's respective and relevant geometry (and/or regardless of how a patient's geometry may change over time) as existing approaches will not support such an approach. The resultant required interaction with a skilled user, in turn, leads to increased cycle-time requirements and a corresponding burden upon the user. Beyond this, such demands upon the availability of skilled planners can ultimately affect the performance of an entire medical-treatment facility and hence the overall quality of treatment across a significant patient population.
Elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions and/or relative positioning of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present invention. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present invention. Certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. The terms and expressions used herein have the ordinary technical meaning as is accorded to such terms and expressions by persons skilled in the technical field as set forth above except where different specific meanings have otherwise been set forth herein.